Storyblok MCP Server for LlamaIndex 9 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Storyblok as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Storyblok. "
"You have 9 tools available."
),
)
response = await agent.run(
"What tools are available in Storyblok?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Storyblok MCP Server
Integrate the powerful headless CMS capabilities of Storyblok directly into your conversational AI. Empower your content teams and developers to organically draft narratives, parse complex asset repositories, and orchestrate page component definitions without relying entirely on the visual editor. Bind your AI local context directly to your Storyblok environment securely, enabling programmatic schema generation and continuous iteration utilizing a streamlined conversational interface designed to accelerate creative velocity.
LlamaIndex agents combine Storyblok tool responses with indexed documents for comprehensive, grounded answers. Connect 9 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Space & Content Discovery — Instantly list active enterprise environments utilizing
list_spacesand fetch broad overarching overviews referencing stories vialist_stories. - Content Construction — Swiftly produce or update textual assets creating schemas directly from prompts invoking
create_content_storyandupdate_content_storysystematically. - Asset & Structure Exploration — Analyze media repositories via
list_assetsand precisely inspect available schema blueprints callinglist_componentsto standardize development. - Risk Management — Exercise safe administrative control over local projects, evaluating internal authorized operators implementing modifications using
list_space_users.
The Storyblok MCP Server exposes 9 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Storyblok to LlamaIndex via MCP
Follow these steps to integrate the Storyblok MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 9 tools from Storyblok
Why Use LlamaIndex with the Storyblok MCP Server
LlamaIndex provides unique advantages when paired with Storyblok through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Storyblok tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Storyblok tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Storyblok, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Storyblok tools were called, what data was returned, and how it influenced the final answer
Storyblok + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Storyblok MCP Server delivers measurable value.
Hybrid search: combine Storyblok real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Storyblok to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Storyblok for fresh data
Analytical workflows: chain Storyblok queries with LlamaIndex's data connectors to build multi-source analytical reports
Storyblok MCP Tools for LlamaIndex (9)
These 9 tools become available when you connect Storyblok to LlamaIndex via MCP:
create_content_story
Provide a name, slug, and content JSON. Creates a new story in a Storyblok space
delete_content_story
This action is irreversible. Permanently deletes a Storyblok story
get_story_details
Retrieves details for a specific content story
list_assets
Lists media assets in a Storyblok space
list_components
Lists available content components
list_space_users
Lists all users with access to a specific space
list_spaces
Lists all accessible Storyblok spaces
list_stories
Requires a space ID. Lists content stories within a specific space
update_content_story
Requires space and story IDs. Updates fields of an existing Storyblok story
Example Prompts for Storyblok in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Storyblok immediately.
"List the recent articles from my Storyblok space and detail their structural components."
"List the structure blueprints by calling list_components and then formulate a new JSON to create a blog story."
"List all multimedia assets in my Storyblok space and display their URLs."
Troubleshooting Storyblok MCP Server with LlamaIndex
Common issues when connecting Storyblok to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpStoryblok + LlamaIndex FAQ
Common questions about integrating Storyblok MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Storyblok with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Storyblok to LlamaIndex
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.